UnLabeled Category Limits Use of Data
After my last post about community college outcomes using data published by the Maryland Higher Education Commission (MHEC), I went in search of more data to get a better sense of the story. I want to know what happens to students that initially enroll in a Maryland community college. I found some data published by the Maryland Longitudinal Data System (MLDS) Center in 2016. It is almost exactly the data I am looking for, unfortunately, there was an unlabeled category that consisted of 25% of the cohort. With this unknown category being such a large part of the pie, I feel that the data is of limited use for really understanding the outcomes of community college enrollees. Despite my belief that the data may be limited, I am going to take a closer look at the data to see if I can learn anything from it.
It is also unclear the time frame after enrollment that the data covers. I know that the population is community college students that were enrolled during the 2008-2009 academic year. I also know that the data was published in 2016. It is unclear if the data set is all students; first-time, full-time students; or another population. I think that my next step may be to contact MLDS Center to clarify what data is being presented. There may have been additional information in the documentation that I have missed.
When I publish graphs in the future I want to be sure to be clear about the data that is presented. In education public policy I feel that this is especially important as the specifics matter. For example, a cohort of first-time full-time students is not the same as a cohort of all community college students.
If Data is Accurate: Few CC STudents Transfer; Fewer earn a BA
If this data is accurate, few community college students transfer to a Maryland four-year institution, only about 6% of the cohort. This is much less than the 12% of the first-time, full-time cohort in the data reported by MHEC. This leads me to believe that the transfer rate in this data source may be underestimated. I wish that I could trust that this data is fully accurate because, unlike the other data source, this data source reports the percent of students that earned a bachelor's from a Maryland four-year institution after transfer. It shows the specific community college to four-year institution pathway to a bachelor's degree that some experts recommend, especially to lower-income students as a method to save money. But it isn't a method to save money if students do not actually end up earning a degree. According to this data, 1% of the students (421 students) that begin at a community college earn a BA from a Maryland four-year institution.
If only 1% of a community college cohort earn a BA, then that pathway to a BA is broken. However, I need to dig into the data more, because other data sources report a slightly less bleak picture. According to data published by the Aspen Institute, which is shown below, nationwide 14% of community college students earn a BA within six years of transfer. However, like the MLDS data I discussed above, I am not sure of the student population, so I need to do more research into the data. Overall I need to better understand the data sources before I make any conclusions.
A Note About THis Data Presentation
As I have said in previous posts I have really been loving playing with data presentation using datawrapper. However, for the multiple pie charts shown above I ran into a few issues. Firstly, the website was being glitchy, not showing changes I had made to the graph and hiding the graph completely. Second, I was not able to make this type of graph look exactly how I wanted it to look. I did not want to show the percent for the grayed-out space and that was not an option because the categories are not the same. This is not surprising, I think that for that type of functionality I would need a graphic designer and not a web application. So while there are a few issues, I am still extremely happy with datawrapper. I am just trying to document for myself the limits of its functionality.
